scan-DocRes/README.md

DEBLUR
ILLUMINATION CORRECT

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# DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks

[![Open in Spaces](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/qubvel-hf/documents-restoration)

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<p align="center">
<img src="images/motivation.jpg" width="400">
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This is the official implementation of our paper [DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks](https://arxiv.org/abs/2405.04408).

## News 
🔥 [2025.7] Our paper ["Aesthetics is Cheap, Show me the Text: An Empirical Evaluation of State-of-the-Art Generative Models for OCR"](https://arxiv.org/abs/2507.15085), which conducts a comprehensive evaluation of SOTA generative models has been online at arXiv. 🔥

🔥 [2025.6] Beyond GPT-4o, we evaluate more SOTA generative models' image generation abilities in various document processing tasks. Check [here](https://github.com/NiceRingNode/Awesome-Image-Generators-for-OCR-Image-Generation-and-Editing)! 🔥

🎉 [2025.5] We evaluate the image generation ability of GPT-4o, including various document processing tasks. Check [here](https://github.com/NiceRingNode/Awesome-Image-Generators-for-OCR-Image-Generation-and-Editing)! 🔥

🎉 [2025.2] Our new work [LGGPT](https://github.com/NiceRingNode/LGGPT) has been accepted to IJCV 2025, an LLM that unifies versatile layout generation tasks! Welcome to follow!

🔥 A comprehensive [Recommendation for Document Image Processing](https://github.com/ZZZHANG-jx/Recommendations-Document-Image-Processing) is available.


## Inference 
1. Put MBD model weights [mbd.pkl](https://1drv.ms/f/s!Ak15mSdV3Wy4iahoKckhDPVP5e2Czw?e=iClwdK) to `./data/MBD/checkpoint/`
2. Put DocRes model weights [docres.pkl](https://1drv.ms/f/s!Ak15mSdV3Wy4iahoKckhDPVP5e2Czw?e=iClwdK) to `./checkpoints/`
3. Run the following script and the results will be saved in `./restorted/`. We have provided some distorted examples in `./input/`.
```bash
python inference.py --im_path ./input/for_dewarping.png --task dewarping --save_dtsprompt 1
```

- `--im_path`: the path of input document image
- `--task`: task that need to be executed, it must be one of _dewarping_, _deshadowing_, _appearance_, _deblurring_, _binarization_, or _end2end_
- `--save_dtsprompt`: whether to save the DTSPrompt

## Evaluation

1. Dataset preparation, see [dataset instruction](./data/README.md)
2. Put MBD model weights [mbd.pkl](https://1drv.ms/f/s!Ak15mSdV3Wy4iahoKckhDPVP5e2Czw?e=iClwdK) to `data/MBD/checkpoint/`
3. Put DocRes model weights [docres.pkl](https://1drv.ms/f/s!Ak15mSdV3Wy4iahoKckhDPVP5e2Czw?e=iClwdK) to `./checkpoints/`
2. Run the following script
```bash
python eval.py --dataset realdae
```
- `--dataset`: dataset that need to be evaluated, it can be set as _dir300_, _kligler_, _jung_, _osr_, _docunet\_docaligner_, _realdae_, _tdd_, and _dibco18_.

## Training 
1. Dataset preparation, see [dataset instruction](./data/README.md)
2. Specify the datasets_setting within `train.py` based on your dataset path and experimental setting.
3. Run the following script
```bash
bash start_train.sh
```


## Citation
```
@inproceedings{zhangdocres2024, 
Author = {Jiaxin Zhang, Dezhi Peng, Chongyu Liu , Peirong Zhang and Lianwen Jin}, 
Booktitle = {In Proceedings of the IEEE/CV Conference on Computer Vision and Pattern Recognition}, 
Title = {{DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks}}, 
Year = {2024}}   
```
## ⭐ Star Rising
[![Star Rising](https://api.star-history.com/svg?repos=ZZZHANG-jx/DocRes&type=Timeline)](https://star-history.com/#ZZZHANG-jx/DocRes&Timeline)